Rare Query Expansion Through Generative Adversarial Networks in Search Advertising
Mu-Chu Lee (Carnegie Mellon University); Bin Gao (Microsoft); Ruofei Zhang (Microsoft)
Generative Adversarial Networks (GAN) have achieved great success in generating realistic synthetic data like images, tags, and sentences. We explore using GAN to generate bid keywords directly from query in sponsored search ads selection, especially for rare queries. Specifically, in the query expansion (query-keyword matching) scenario in search advertising, we train a sequence to sequence model as the generator to generate keywords, conditioned on the user query, and use a recurrent neural network model as the discriminator to play an adversarial game with the generator. By applying the trained generator, we can generate keywords directly from a given query, so that we can highly improve the effectiveness and efficiency of query-keyword matching based ads selection in search advertising. We trained the proposed model in the clicked query-keyword pair dataset from a commercial search advertising system. Evaluation results show that the generated keywords are more relevant to the given query compared with the baseline model and they have big potential to bring extra revenue improvement.